The invention is concerned with the assessment of images obtained from functional medical scanning procedures such as Positron Emission Tomography (PET) or Single Photon Emission Computed Tomography (SPECT) and, in particular, with the comparison of such images with a set of reference images.
Alzheimer's Disease (AD) is becoming one of the major health concerns in developed countries that have ageing populations. As Positron Emission Tomography is believed to be a useful imaging modality for diagnosis operations related to AD, the USA and some European countries have started reimbursing this type of examination, in the case of the USA, to differentiate AD from Fronto-Temporal Dementia (FTD).
PET can be used with a variety of tracers. The resulting images will show different physiological functions. 2-[18-F]fluoro-2-deoxy-D-glucose (FDG) has been extensively utilized in PET in several clinical situations as a marker of glucose metabolism. In neurology, uptake of glucose in the brain has been correlated to brain activity and function.
In the case of the diagnosis of Alzheimer's Disease, low activity patterns in some lobes of the cortex can be interpreted as indicative of Alzheimer's disease. Due to several factors, the interpretation of FDG brain scans remains challenging and for AD, only autopsy results are considered the most reliable test for AD. Therefore, most cases are currently diagnosed as “probable AD”. The difficulty resides in the identification of the dementia itself, which with FDG PET could be confirmed to a more reliable level, and more importantly, the differentiation between different types of dementia, as each type is covered with different patient management (medication, care, etc.).
In a clinical context, the clinician will use a number of elements to perform a diagnosis. These include various neurological tests, psychological tests (such as the Mini Mental State Examination or MMSE) and assessment based on PET or SPECT images of the brain. The FDG PET image reveals patterns of glucose metabolism and the SPECT image shows patterns of perfusion (amount of blood supply): in both cases, abnormal levels of perfusion or metabolism can be indicators of dementia.
When a clinician assesses, for example, a PET image of the brain for potential brain diseases, he or she will be looking at patterns of hypo-metabolism (low intensity on the PET image) or hyper-metabolism (high intensity on the PET). The symmetry of the brain is also an important factor. FIG. 1 shows two FDG-PET images: one of a normal case (left) and one of an advanced AD case (right). For each case, an axial (top), coronal (middle) and sagittal (bottom) slice is shown. The arrows point to the areas of hypo-metabolism noticeable in the PET image.
A range of software tools have been developed to help clinicians reach a diagnosis. These include Neurogam (produced by Segami Corporation, 8325 Guilford Road, Suite B, Columbia, Md. 21046, USA), NeuroQ (produced by Syntermed, Inc., Tower Place Center, 3340 Peachtree Road, NE, Suite 1800, Atlanta, Ga. 30326) BRASS (produced by Hermes Medical Solutions, Skeppsbron 44, 111 30 Stockholm, Sweden) and Scenium (produced by Siemens Medical Solutions, Siemens House, Oldbury, Bracknell, Berkshire RG12 8FZ, United Kingdom) for the commercially available products and NeuroSTAT (Prof. Minoshima, University of Washington, USA) and SPM (University of Washington, School of Medicine, Radiology, 1959 N.E. Pacific Street, RR215, Box 357115, Seattle, Wash. 98195-7115, United States) for the academic packages. SPM stands for Statistical Parametric Mapping and designates both the software package and the class of methods using statistical parametric maps.
All available tools follow a similar workflow and processing algorithm with two variants: the analysis is either voxel-based (Neurogam, SPM, NeuroSTAT) or ROI-based (NeuroQ, BRASS). Examples of both approaches are described in the following sections.
All methods involve registering all available data spatially to a reference dataset so that one can assume that physiologically corresponding regions are in spatial correspondence between patients. All methods also involve collecting a set of “normal” images (or control cases) and evaluating their statistical properties as a group.
Using SPM, a group of normal images is collected and registered to a standard spatial reference. The images are then smoothed, normalized for intensity scale (using various methods) and a mean and standard deviation volumes are created. These two volumes, considered as a pair, are called the statistical reference.
Using Gaussian random field theory and assuming the local intensities in normal cases follow a Gaussian distribution, one can calculate the probability of any pixel intensity in a test image (a patient case) exceeding a certain intensity value.
Assessing a test image comprises the following steps:                the test image is registered to the reference;        the test image is smoothed;        the test image is normalized in intensity; this involves rescaling the intensities in the image using as a reference either the average intensity in a particular region or the average intensity in the whole brain with some optional variations.        
At this stage, the test image is deemed comparable to the statistical reference and can then be positioned against the mean and standard deviation volumes.                the corresponding Z-scores (Z=(X−mean)/standard deviation) are calculated for each voxel, creating a Z-volume; Z-scores are also occasionally called T-scores depending on the exact mathematical hypotheses made. The two terms will be used indifferently in this document.        the maximal Z-score is calculated in the volume. Volumes that bear absolute Z-scores above a threshold corresponding to a pre-determined p-value for the volume can be considered abnormal with a certain confidence rating (usually 95%, for a p-value of 0.05).        
In practical terms, the volumes considered abnormal are classified as potential AD cases.
SPM has the advantage that the mathematical basis of this approach is sound and based on simple hypotheses. Moreover, abnormality is measured on a voxel-basis.
On the other hand, the Gaussian model is most probably not very well realized; therefore the accuracy of the computed p-values may be illusory (i.e. it works perfectly in the ideal case but it is not clear what happens when there are deviations from the model).
Because the image is not calibrated to real physiological values of uptake, the process necessitates a linear intensity normalization step: the intensity values are divided by the average intensity in a pre-selected reference region of the brain. A number of variations exist including robust means or other mathematical tools. The purpose of this step is to try to measure the physiological activity represented by the intensity where the measured intensity value has been affected by factors such as patient weight, injection dose, etc. This makes the algorithm inevitably sensitive to variations in the regions used as a reference. The idea is similar to that of Standard Uptake Value (SUV) used routinely in clinical PET for oncology. SUV is not deemed to be sufficiently robust for neurology, which is why a more specific method needs to be implemented.
The fine detail of the abnormality map is sensitive to registration artefacts or inaccuracies.
The smoothing applied implies that the Z-maps are extremely regular and therefore, region-based assessment may be more relevant than observation of individual voxel Z-scores.
The NeuroQ approach to ROI scoring is similar to the one previously described except that intensities are measured as the average intensity in each ROI (usually, 10 to 100 ROIs are considered). Typically, these regions of interest correspond to anatomically relevant regions (see FIG. 2). Each ROI has thus a normal range of intensities (described by a mean and standard deviation pair). The intensities of the patient ROIs are positioned in relation with that of the statistical reference. Any ROI for which the Z-score exceeds a preset threshold is considered abnormal.
Previous work in this field has used selected ratios of ROI average intensities to assist in the analysis of medical images (e.g. Herholz K, Adams R, Kessler J, Szelies B, Grond M, Heiss W D, Criteria for the diagnosis of Alzheimer's disease with positron emission tomography, Dementia 1990; 1:156-164).
Another publication, Herholz K, Perani D, Salmon E, Franck G, Fazio F, Heiss W D, Comar D. (1993) Comparability of FDG PET studies in probable Alzheimer's disease. Journal of Nuclear Medicine 34:1460-1466, describes how ratios of carefully selected relevant regions have been computed as well as inhomogeneous ratios (metabolic ratio) involving blood measurements.